Registration may be described as a process of aligning geographic sensor data (the “source data set”) to a known set of reference data (the “target data set”). The source data set may originate from various onboard sensors and, due to the inherent errors in the sensors used to create the source data set, this data may be shifted (x, y, z) and rotated (pitch, roll, yaw) relative to truth data set (trusted to be accurate). The target data set may originate from a trusted database considered accurate within a tolerance. Digital Terrain Elevation Data (DTED) may be one example of a truth data set. A method for registration solution may determine the transformation which may align the erroneous source data set to the reference target data set.
The task of aligning the two independent data sets or streams that are measurements of a three dimensional (3D) environment (additionally, a four dimensional (4D) environment if the environment is time varying) to locate a body in space may remain challenging. Specifically so in instances where specific degrees of freedom are not observable in the transformation from a source data set and target data set. Any registration attempt may be fundamentally limited by the content of the environment being measured. Any six Degrees of Freedom (DOE) (e.g. x, y, z, pitch (Θ), roll (φ)), and yaw (ψ))) registration solution used to align two flat planes to one another may provide erroneous results for the in-plane, translational degrees of freedom. While this is just one example in which the registration solution does not have observability into specific DOF, other situations that affect any combination of DOF may be constructed.
Recent advances in mobile robot and unmanned vehicle technology have driven rapid development in the broader area of machine vision. A variety of ranging sensors exist that may generate 3D point cloud data. This has led to significant advances in 3D point cloud processing and rendering. While there has been significant advancement in the development of methods to align (or register) point cloud data with a variety of data sources (surface maps, imagery, point cloud data), there remain no accurate solutions to the practical issue of poor point cloud alignment or registration results handled in a real-time application.
Some attempts at point cloud registration may assume a successful registration is possible for each set of corresponding data. In some cases, certain degrees of freedom in the registration problem may be unobservable, or poorly conditioned. One specific instance in which this problem arises is when the 3D environment being measured is completely devoid of features. Applicable here is the above example attempting to register a flat plane with a flat plane realized when operating in a featureless environment that can be approximated as a plane. The result of trying to register unobservable degrees of freedom may be incorrectly computing the location of non-terrain features, which in most cases are the very obstacles that a pilot is trying to avoid. This erroneous positioning of obstacles without proper annunciation that the data is degraded may provide Hazardously Misleading Information (HMI) to the pilot.
Therefore, in a real-time application, a desirable system and method may accommodate varying scanned environments by intelligently enabling/disabling specific degrees of freedom from the registration solution based on an objective measure of environment observability and confidence in the registration solution.